Supervised Object Knowledge Learning for Image Understanding
Anna Bosch, Xavier Muñoz, Jordi Freixenet, Joan Martı́ · 2004
Abstract: An object learning system for image understanding is proposed in this paper. The knowledge acquisition system is designed as a supervised learning task. Therefore, the role of the user as teacher of the system is emphasized, which allows to obtain the object description as well as to select the best recognition strategy for each specific object. An object description is acquired by considering different model representations provided from several representative examples in training images. Moreover, different recognition strategies are built and applied to obtain initial results. Next, teacher evaluates these results and the system automatically selects the specific strategy which best recognise each object. We provide a feedback with the user until he/she is satisfied with the knowledge achieved by the system. Experimental results are shown and discussed. Key words: image understanding, supervised learning, object recognition, srategy selection, image processing.